Video camera systems have long been used to monitor areas or regions of interest for the purposes of maintaining security and the like. One important application is the use of video camera systems to monitor sensitive areas in locations such as museums or art galleries which include valuable items that could be potentially removed by a member of the public. Typically such a system would include a number of video cameras which would be monitored by a security attendant. In this human based scenario, the attendant would be relied on to detect any changes in the areas being viewed by each of the individual cameras. Clearly, this approach has a number of significant disadvantages. Notwithstanding the expense of the labour involved, this approach is prone to human error as it relies on the ability of the attendant to detect that a change of significance has occurred within the area being viewed by the camera without being distracted by any other visual diversion.
With the advent of more sophisticated image processing algorithms, and the associated computer hardware to implement these algorithms in real time, a number of attempts have been made to automate this process. A naïve approach to this problem includes the direct comparison of either individual or groups of pixel intensities of subsequent sequential images or frames which make up a digital video signal. If the difference between a group of pixels over a number of sequential images is found to be over some threshold then an alarm is generated indicating that movement has occurred within the area being viewed by the camera. Clearly, this naïve approach when applied to a viewing area which naturally includes a subset of objects moving within it (e.g. patrons at a museum) and a number of stationary items (e.g. museum exhibits) fails as the movement of patrons will trigger the alarm.
One attempt to overcome this disadvantage is to apply background modelling techniques to the subsequent images or frames corresponding to the area being viewed by the camera. In this approach, portions of the image which do not change substantially from normal from image to image are determined to be part of the background. In the example of an art gallery or museum, the paintings or artefacts would form part of the “background” of an image as they are stationary in the subsequent images or frames of the digital video signal. If one of the “background” pixels corresponding to an artefact has an intensity which varies above a predetermined threshold then this pixel is in alarm condition. However, as would be appreciated by those skilled in the art, this approach is extremely sensitive to pixel intensity changes as would typically be caused by lighting changes resulting from shadows, time of day variation and other ambient light variation. Whilst some of these effects can be compensated by employing a more sophisticated background model, this also increases the overall complexity and tuning requirements of the surveillance system. Another disadvantage of the background modelling approach and other prior art detection systems is that they fail where there is a temporary total occlusion of an object of interest or in the case where there is permanent partial occlusion of the object.
In a related area of application, various detection or monitoring systems which may be arranged to provide security or detect and measure the behaviour of objects within a field of view or detection region of the system are well-known. Examples range from Doppler radar detectors used to measure or detect a characteristic such as the speed of vehicles and active beam detectors which measure or detect a characteristic such as the reflection of an incident beam off an object to devices such as passive infra-red (PIR) detectors which measure the characteristic of heat emanated by objects and are often used in security applications. A requirement of each of these devices is that they may be orientated to inspect a predetermined field of view which corresponds to the detection region of the device.
Clearly, the performance of these devices may be degraded or totally compromised if the actual field of view or detection region is different from that assumed during initial setup. In the example of a Doppler radar detector, the characteristic of speed calculated by the device will depend on the angle of travel of the moving vehicle with respect to the orientation of the detector and errors in setup may result in erroneous results.
In the example of a PIR detector, this device may typically be located and adjusted to view regions which are required to be kept secure such as an entranceway to a building or the like. If in fact the PIR detector is not pointing in the correct direction, a person moving along the viewed entranceway may not be detected, as they may not be within the field of view of the detector.
This illustrates a significant disadvantage with devices of this nature which have a detection region set by the orientation of the device. A person wishing to gain access to a building may during the day, when the PIR detector is inactive, change the detecting direction of the device so that it no longer points towards or views a given detection region. Accordingly, when the device becomes operative at night it may no longer be pointing in the correct direction thereby allowing an intruder to potentially gain access to the building. Similarly, a radar detector which has been positioned to detect the speed of vehicles moving in a given direction may provide incorrect results if it has been tampered with by changing its detecting direction.
Any discussion of documents, devices, acts or knowledge in this specification is included to explain the context of the invention. It should not be taken as an admission that any of the material forms a part of the prior art base or the common general knowledge in the relevant art in Australia or elsewhere on or before the priority date of the disclosure herein.
It is an object of the present invention to provide a method that enables detection of an object in a sequence of images which compensates for temporary total occlusion of the object.
It is a further object of the present invention to provide a method that enables detection of an object in a sequence of images which compensates for permanent partial occlusion of the object.
It is yet still a further object of the present objection to provide a method which can be implemented in real time on a digital video system or signal.
It is also an object of the present invention to provide a detection system capable of monitoring its operation and hence whether tampering or at least unauthorised alteration of the system has taken place.